Abstract
In fault level identification, there are ordinal structures between different levels, and some features have the monotonic dependency with decision. In this paper, we propose a feature selection algorithm for fault level identification based on ordinal classification. First, we design a new feature evaluation function to evaluate the quality of features based on ordinal rough set. Second, combining with the search strategy of genetic algorithm (GA), an improved feature selection algorithm is proposed. Finally, the proposed feature selection algorithm is employed to crack level identification. Experimental results show that the proposed algorithm not only can reduce the feature dimension but also improve the accuracy of identification.
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References
Ben-David, A., Sterling, L., Pao, Y.H.: Learning and Classification of Monotonic Ordinal Concepts. Blackwell Publishers (1989)
Gutiérrez, P.A., Pérez-Ortiz, M., Sánchez-Monedero, J., et al.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28, 127–146 (2016)
Lei, Y., Zuo, M.: Gear crack level identification based on weighted K nearest neighbor classification algorithm. Mech. Syst. Signal Process. 23(5), 1535–1547 (2009)
Zhao, X., Zuo, M.J., Patel, T.H.: Generating an indicator for pump impeller damage using half and full spectra, fuzzy preference-based rough sets and PCA. Meas. Sci. Technol. 23(4), 45607–45616 (2012)
Zhang, L., Huang, W., Xiong, G.: Assessment of rolling element bearing fault severity using multi-scale entropy. J. Vib. Shock 33(9), 185–189 (2014)
Hu, Q., Pan, W., Zhang, L., et al.: Feature selection for monotonic classification. IEEE Trans. Fuzzy Syst. 20(1), 69–81 (2012)
Pan, W., Hu, Q.: An improved feature selection algorithm for ordinal classification. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 99(12), 2266–2274 (2016)
Pan, W., He, H.: An ordinal rough set model based on fuzzy covering for fault level identification. J. Intell. Fuzzy Syst. 33(5), 2979–2985 (2017)
Hu, Q., Che, X., Zhang, L.: Rank entropy-based decision trees for monotonic classification. IEEE Trans. Knowl. Data Eng. 24, 2052–2064 (2012)
Frank, E., Hall, M.: A simple approach to ordinal classification. In: Proceedings of the 12th European Conference on Machine Learning, pp. 145–156 (2001)
Xia, F., Zhang, W., Li, F., et al.: Ranking with decision tree. Knowl. Inf. Syst. 17(3), 381–395 (2008)
Acknowledgements
This work is supported by Youth Project of Fujian Province of China (Grant No. JAT160350).
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Pan, W. (2020). An Improved Feature Selection Algorithm for Fault Level Identification. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_12
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DOI: https://doi.org/10.1007/978-981-13-9406-5_12
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